High-Accuracy 3D Reconstruction of Underwater Objects from Forward-Looking Sonar Using Shape-Specific Synthetic Training Data

Forward-looking sonar (FLS) is robust to low illumination and turbidity, whereas its range–azimuth projection collapses elevation information, making single-view three-dimensional reconstruction inherently ambiguous. This study investigates how the geometric composition of synthetic training data influences reconstruction accuracy for cylindrical and spherical objects. Paired acoustic-view and front-view depth images were generated in Blender, and the synthetic acoustic-view images were translated toward the real-sonar image domain using CycleGAN. The CycleGAN-translated images were used to train the previously proposed acoustic-view-to-front-view network (A2FNet) to estimate pseudo-front-view inverse-depth maps. In addition to evaluating shape-specific training data, this study analyzes geometry-dependent large-error cases and uses the identified failure patterns to revise the synthetic cylinder training-scene configuration. Four models were trained using cylinder-only, sphere-only, mixed cylinder–sphere, or previous-study data, with 4000 training images per model. A total of 500 test images were prepared for each of four simulation-derived environments. Across the four environments, the best geometry-specific or mixed models reduced the mean scaled symmetric squared Chamfer distance (CD) from 77.3280–90.8582 for the previous-study model to 1.8341–7.7329. Large-error analysis revealed recurring incomplete target observations shared by the cylinder-specific and mixed models. Retraining with the revised cylinder dataset further reduced the mean CD to 1.0336–4.8036 and the maximum CD to 5.3346–11.9897 across the six evaluated model–environment combinations. These results indicate that reconstruction accuracy within the evaluated simulation-based pipeline is strongly influenced by the geometric composition and scene design of the synthetic training data. Systematic analysis of reconstruction failures can therefore provide useful guidance for revising synthetic training scenes and reducing large reconstruction errors.